Instructions to use driftbench/sst2_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driftbench/sst2_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="driftbench/sst2_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("driftbench/sst2_base") model = AutoModelForSequenceClassification.from_pretrained("driftbench/sst2_base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cdc6e4825df38600c5677e28415256a9c545b6b3a9ebc20c90ff861460f917e4
- Size of remote file:
- 3.52 kB
- SHA256:
- 2973bb9490ce283455142c79bfcfaa974308b08479009fe27af60dfb6c059539
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